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A Clinical Prediction Model for Invasive Pulmonary Fungal Disease in Dialysis Patients: Development, Validation, and
Jiao Yang1,2, Dongmei Wang3, Meng Li1
1Department of Pharmacy, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
Introduction:
Patients receiving maintenance hemodialysis (MHD) are particularly vulnerable to fungal infections attributable to immunocompromised status, frequent therapeutic interventions, and environmental exposure. Notably, invasive pulmonary fungal infection (IPFI) carries particularly high morbidity and mortality rates. The non-specific clinical manifestations coupled with inconclusive microbiological findings frequently result in diagnostic delays. Compounded by the narrow therapeutic window, these factors pose significant clinical challenges and adversely affect patient outcomes. Typical misdiagnosis rates for IPFI in MHD patients exceed 50%, with median diagnostic delays of 10-14 days, contributing to mortality rates up to 40% from delayed antifungal therapy. The aim of this study was to develop and validate a clinical prediction model for identifying IPFI in MHD patients.
Methods:
This retrospective cohort study included MHD patients with pneumonia admitted between January 2022 and December 2023 for the modeling cohort (IPFI group, n = 51; non-IPFI group, n = 951). An external validation cohort consisted of patients admitted from January to December 2024 (IPFI group, n = 22; non-IPFI group, n = 463). Predictors were screened from 22 clinical variables using least absolute shrinkage and selection operator (LASSO) regression, and a prediction model was constructed using logistic regression. Predictors were standardized (z-score normalization) before LASSO to ensure comparability.
Results:
A final four-variable model was developed, including highest body temperature, hypothyroidism, pancytopenia, and anti-neutrophil cytoplasmic antibody (ANCA)-associated disease. The model demonstrated strong discrimination, good calibration, and strong performance in the calibration plot. The model showed good internal validity (optimism-corrected AUC = 0.864; calibration slope = 0.97) and maintained robust performance in external validation (AUC = 0.877, 95% CI: 0.800-0.948; calibration slope = 1.12, 95% CI: 0.828-1.511). In the LASSO path, pancytopenia showed the highest importance (standard β=0.342), followed by maximum body temperature (standard β=0.289), indicating stronger effect sizes for these variables. A nomogram was subsequently developed based on the model.
Conclusion:
This prediction model effectively identifies MHD patients with pneumonia at high risk of developing IPFI, demonstrating robust predictive performance. This tool can facilitate early risk stratification by clinical pharmacists in ward decision-support and stewardship consultations, optimizing the prophylactic and targeted use of antifungal agents and promoting rational drug therapy.
Insights
This study developed a prediction model to identify patients on maintenance hemodialysis (MHD) at high risk for invasive pulmonary fungal infections (IPFI). The model uses four clinical variables to improve early diagnosis and treatment of IPFI in this vulnerable population.
Area of Science:
- Infectious Diseases
- Nephrology
- Medical Informatics
Background:
- Patients on maintenance hemodialysis (MHD) are highly susceptible to invasive pulmonary fungal infections (IPFI) due to compromised immunity and frequent interventions.
- IPFI in MHD patients presents with non-specific symptoms and inconclusive microbiology, leading to diagnostic delays, misdiagnosis rates over 50%, and high mortality (up to 40%).
- Early and accurate identification of IPFI is crucial for timely antifungal therapy and improved patient outcomes in the MHD population.
Purpose of the Study:
- To develop and validate a clinical prediction model for the early identification of invasive pulmonary fungal infections (IPFI) in patients undergoing maintenance hemodialysis (MHD).
- To improve diagnostic accuracy and reduce delays in IPFI detection among MHD patients.
- To provide a tool for risk stratification to guide antifungal therapy and optimize drug use.
Main Methods:
- A retrospective cohort study utilizing two cohorts: a modeling cohort (2022-2023) and an external validation cohort (2024).
- Inclusion criteria: Maintenance hemodialysis patients admitted with pneumonia.
- Predictors were identified using least absolute shrinkage and selection operator (LASSO) regression from 22 clinical variables, followed by logistic regression for model construction. Standardization (z-score) was applied to predictors before LASSO regression.
Main Results:
- A four-variable prediction model was developed, incorporating highest body temperature, hypothyroidism, pancytopenia, and anti-neutrophil cytoplasmic antibody (ANCA)-associated disease.
- The model demonstrated strong internal validity (optimism-corrected AUC = 0.864) and robust external validation performance (AUC = 0.877).
- Pancytopenia and maximum body temperature were identified as the most significant predictors in the model.
Conclusions:
- The developed prediction model effectively identifies MHD patients with pneumonia at high risk for IPFI, showing strong and validated predictive performance.
- This tool can aid clinical pharmacists in early risk stratification, supporting ward decision-making and antifungal stewardship.
- Implementation of this model can optimize the prophylactic and targeted use of antifungal agents, promoting rational drug therapy in MHD patients.
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